Ahmed Mai M, Donia Abd El Raheim M, Ismail Yassin, Youssef Fadia S, El-Ahmady Sherweit H
Department of Environmental Medical Sciences, Faculty of Graduate Studies and Environmental Research, Ain Shams University, Cairo, Egypt.
Natural Products Unit, Department of Medicinal and Aromatic Plants, Desert Research Center, Cairo, Egypt.
Sci Rep. 2025 Sep 23;15(1):32652. doi: 10.1038/s41598-025-17113-y.
Legume Seeds of Vicia are cultivated and consumed worldwide for their nutritional value and bioactive compounds. Notably, Vicia faba (fava bean) seeds, with their many cultivars or varieties, are deeply rooted in cuisines of the Middle East and across the globe. In this work, simple and fast spectroscopic techniques, including UV and FT-IR spectroscopy, were used in combination with multivariate statistical techniques not only to discriminate among different varieties of fava beans but also to distinguish them from other Vicia legumes, such as Vicia sativa and Vicia monantha. In addition, the total phytochemical phenolics and flavonoids, and in vitro radical scavenging activity were assessed. Preliminary exploratory data analysis using PCA on both UV and FT-IR spectra was capable of distinguishing the seeds of fava bean varieties from other Vicia species. On the other hand, the FT-IR was limited in distinguishing between the varieties of fava beans compared to the UV spectra. Therefore, UV spectra were subjected to unsupervised techniques, PCA and HCA, and supervised classification techniques, SIMCA and PLS-DA, to construct useful discrimination models for eight varieties of fava beans. PCA and HCA successfully segregated the eight fava bean varieties into three informative clusters: the first cluster for the five traditional commercial Egyptian varieties, the second cluster for the two new Egyptian varieties Maryout 2 and 3, and the third cluster for the Spanish variety Luz de Otoño. Furthermore, SIMCA and PLS-DA models demonstrated good separation among these three classes of fava beans, with 100% classification accuracy for the validation set samples. In addition, the varieties of fava beans and other Vicia species showed a diverse content of Phenolics, flavonoids, and radical scavenging capacity, with the traditional Egyptian varieties of Sakha4 and Giza 843, as well as Vicia sativa and Vicia monantha, being the best. In conclusion, for the first time, UV spectroscopy combined with multivariate techniques could serve as a simple and fast method to distinguish between some Vicia seeds. Additionally, Vicia sativa, Vicia monantha, and the fava bean varieties Sakha 4 and Giza 843 might be superior to others in developing functional foods and phytopharmaceuticals.
巢菜属豆科植物种子因其营养价值和生物活性化合物而在全球范围内被种植和食用。值得注意的是，蚕豆（野豌豆）种子有许多栽培品种或变种，深深扎根于中东和全球各地的美食之中。在这项工作中，简单快速的光谱技术，包括紫外光谱和傅里叶变换红外光谱，与多元统计技术相结合，不仅用于区分不同品种的蚕豆，还用于将它们与其他巢菜属豆类，如窄叶野豌豆和单花野豌豆区分开来。此外，还评估了总植物化学酚类和黄酮类化合物以及体外自由基清除活性。使用主成分分析（PCA）对紫外光谱和傅里叶变换红外光谱进行初步探索性数据分析，能够将蚕豆品种的种子与其他巢菜属物种区分开来。另一方面，与紫外光谱相比，傅里叶变换红外光谱在区分蚕豆品种方面存在局限性。因此，对紫外光谱应用无监督技术主成分分析（PCA）和层次聚类分析（HCA）以及监督分类技术软独立模型类比法（SIMCA）和偏最小二乘判别分析（PLS-DA），以构建八个蚕豆品种的有效判别模型。主成分分析（PCA）和层次聚类分析（HCA）成功地将八个蚕豆品种分为三个有信息价值的聚类：第一个聚类为五个传统的埃及商业品种，第二个聚类为两个新的埃及品种玛丽奥特2号和3号，第三个聚类为西班牙品种卢兹·德·奥托尼奥。此外，软独立模型类比法（SIMCA）和偏最小二乘判别分析（PLS-DA）模型在这三类蚕豆之间表现出良好的分离效果，验证集样本的分类准确率为100%。此外，蚕豆品种和其他巢菜属物种的酚类、黄酮类化合物含量和自由基清除能力各不相同，传统的埃及品种萨哈4号和吉萨843号以及窄叶野豌豆和单花野豌豆表现最佳。总之，紫外光谱结合多元技术首次可以作为一种简单快速的方法来区分一些巢菜属种子。此外，窄叶野豌豆、单花野豌豆以及蚕豆品种萨哈4号和吉萨843号在开发功能性食品和植物药物方面可能优于其他品种。
